The present invention is related to providing information regarding a user's on-line interactions that are performed over a computer network, such as the Internet. It especially pertains to providing information regarding user behavior targeting, e.g., advertisement targeting.
In general, targeting engines operate to profile on-line users and visitors with specific affinities and interests derived from their actual online behavior so that users with particular profiles may be affectively targeted with particular on-line advertisements. A user profile may depend on any number of user actions on-line or over a computer network, such as viewing a web page, clicking on an advertisement, performing a web search based on one or more keywords, etc. A user profile may include scores in particular categories or a user's areas of interest, such as electronics, travel, automotive, etc. After users are profiled, advertisements may then be targeted or displayed to such users based directly on how the targeted users scored in each category.
Although behavior targeting processes typically provide users scores that are helpful to then target the users who would be most likely to purchase particular products or services, some behavior targeting processes can produce inaccurate user scores. That is, a behavior targeting process may provide user scores that do not accurately correlate to a user's actual preferences. Categorizing users incorrectly can cause irrelevant advertisements to be displayed to these incorrectly categorized users, which results in huge revenue loss.
Accordingly, it would be beneficial to provide mechanisms and techniques to ensure that the targeting engine is correctly categorizing users based on their on-line behavior and/or to be able to detect and trace errors in an efficient and accurate manner. It would especially be beneficial to provide information regarding user on-line behavior as it pertains to a particular user's or set of users' scores.